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Aprimoramento da relevância clínica dos modelos de predição de hemorragia em trauma

  • role： Corresponding author, First author通信作者, 第一作者
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK

  • Email：sankalp.tandle1@nhs.net
  • Introduction：*sankalp.tandle1@nhs.net
Sankalp Tandle12*,  
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK

Jared M. Wohlgemut12,  
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, Birmingham B15 2WB, UK

Max E. R. Marsden13,  
  • Affiliation：

    Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

Erhan Pisirir4,  
  • Affiliation：

    Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

Evangelia Kyrimi4,  
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK

Rebecca S. Stoner12,  
  • Affiliation：

    Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

William Marsh4,  
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK

Zane B. Perkins12,  
  • Affiliation：

    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK

    The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK

    Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, Birmingham B15 2WB, UK

Nigel R. M. Tai123

Resumo

Palavras-chave

Trauma;injury;Blood transfusion;Massive transfusion;prediction;Artificial intelligence;Machine learning

References

  1. 1..
    Peng HT, Siddiqui MM, Rhind SG, Zhang J, Teodoro da Luz L, Beckett A. Artificial intelligence and machine learning for hemorrhagic trauma care. Mil Med Res. 2023;10(1):6.
  2. 2..
    El-Menyar A, Mekkodathil A, Abdelrahman H, Latifi R, Galwankar S, Al-Thani H, et al. Review of existing scoring systems for massive blood transfusion in trauma patients: where do we stand? Shock. 2019;52(3):288–99.
  3. 3..
    Ho AM, Zamora JE, Holcomb JB, Ng CS, Karmakar MK, Dion PW. The many faces of survivor bias in observational studies on trauma resuscitation requiring massive transfusion. Ann Emerg Med. 2015;66(1):45–8.
  4. 4..
    Holcomb JB, Moore EE, Sperry JL, Jansen JO, Schreiber MA, Del Junco DJ, et al. Evidence-based and clinically relevant outcomes for hemorrhage control trauma trials. Ann Surg. 2021;273(3):395–401.

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